THE OPEN DATA LAKEHOUSE FOR TRUSTED AI

Ying Chen, Product Manager
Ian Brooks, Principal Sales Engineer

Advanced ML can be challenging, but with the latest innovations in ML and data engineering tooling, we will show you how to level up your predictive analytics game across your whole business. In this session, we will explore new features designed to improve data practitioner workflows to support the rapid development of enterprise AI applications on Cloudera’s open data lakehouse.

We will cover the specific enhancements made to model management, focusing on the model registry, and discuss their real-world applications. We will also discuss the new Interactive Sessions and Virtual Cluster types, and their importance in refining data engineering development workflows.

Key takeaways:

• Understand the improvements in model development and management and how they apply to real-world scenarios.

• Learn about the new Interactive Sessions and Virtual Cluster types and their role in streamlining data engineering development workflows.

• Discover how these and other updates enable efficient and effective delivery of trusted enterprise data and AI solutions.

Navita Sood, Director Product Marketing, MDA
Vineeth Varughese, Sr. Product Marketing Manager

Trusted AI needs trusted data from a trusted data lakehouse. Cloudera’s open data lakehouse now makes it easier than ever to turn your data into insight at scale, everywhere. No more compromises as CDP Private Cloud Base now adds support for Apache Iceberg, letting you deploy the same great analytics wherever you need them, across hybrid cloud.

In this session we will cover the key use cases that Cloudera’s open data lakehouse enables for organizations to become data-driven while improving data user efficiency and reducing costs. We’ll show you how, regardless of where your data is or what you are using to get value from it now, Cloudera lets you turn it into value faster with a platform that always operates best thanks to Cloudera Observability. You’ll see Cloudera’s Open Data Lakehouse in action and hear about the benefits customers are reaping. 

Key takeaways:

• Cloudera’s open data lakehouse is now available for private cloud and can move with you across hybrid cloud as needed

• Discover the latest data lakehouse use cases that let you reap insight and value 

• Learn how Cloudera Observability makes Cloudera deployments always operate best

• See how it’s now easier than ever to move to Cloudera’s open data lakehouse on premises.

Tim Spann, Principal Developer Advocate, Data-in-Motion

In this session, we will explore how customers are building high-performance data pipelines with Cloudera’s Data-in-Motion capabilities to execute on their AI initiatives. We will focus on both generative AI as well as the process of building and deploying traditional ML against data of increasing velocity. 

Learn how to "own" data ingestion into vector databases for security and efficiency with a new NiFi processor exclusive to Cloudera customers that vectorizes data with connectors to popular vector databases. We will also cover how to use SQL to enable self-service analytics against streaming and batch data and how the resulting data can be used to drive ML efforts, including real-time scoring.  

Key takeaways:

• Understand how to solve for ingestion into vector databases to drive generative AI

• Discover how to ensure data pipeline security for trusted AI

• Learn how to use SQL to accelerate traditional ML efforts from exploration through to real-time scoring for critical use cases

Manish Maheshwari, Director, Product Management

Keeping track of what runs where on which clusters, how it performs, and how or whether it could perform better is a big undertaking. Or rather, it used to be, but not anymore. Cloudera is proud to reveal its first applied observability solution that helps you understand and optimize your Cloudera Data Platform deployments across your data lakehouse.

Workload-Aware Autoscaling delivers a brand new compute autoscaling capability that allows your virtual data warehouse to be sized and used for diverse data storage and analytical workloads. This enables dynamically and on-demand, according to CPU and memory utilization, and effective parallelism required to deliver results for your business quickly.

Key takeaways:

• Efficient SLA and cost-bound autoscaling and multithreading 

• Effective and efficient hardware utilization of cloud provider compute instances 

• Diverse workload management in a single VW to improve usability and workload management

Manish Maheshwari, Director, Product Management

Apache Kudu is an open source distributed data storage engine that makes fast analytics on fast and changing data easy. Therefore, the combination of Apache Impala and Apache Kudu make a great combination for real-time analytics on streaming data for time series and real-time data warehousing use cases. In this session, we will discuss how you can now connect Apache Impala in Cloudera Data Warehouse service with Apache Kudu clusters running in Data Hub.

Key takeaways:

• Get a primer on Apache Kudu, supported use cases, and customer case studies 

• Learn how to integrate Apache Impala in CDW and and Kudu in Data Hub to achieve separation of compute (i.e., Impala) and storage (i.e., Kudu)

• Learn the benefits of using Impala in CDW and scale up compute and storage independently on-demand